1. Field of the Invention
The present invention relates to a machine learning apparatus and method for learning a condition associated with the number of corrections for any command of a position command, a speed command, or a current command used to control a motor, and a motor control apparatus including the machine learning apparatus.
2. Description of the Related Art
The smoothness of feed of a feed axis of a machine, such as a machine tool, can be quantitatively grasped using the number of errors between a position command relative to a rotor of a motor and an actual position of a feed mechanism unit including the motor for operating the feed axis and various tools annexed thereto, and for example, the more the smoothness of feed of the feed axis of the machine deteriorates, the greater the number of errors becomes. The actual position of the feed mechanism unit includes an actual position of a machining unit that is obtained by an external sensor (linear scale) when a full-closed control is applied and an actual position of the rotor that is obtained by a pulse encoder provided to the motor when a semi-closed control is applied.
There are various deterioration factors in the smoothness of feed of the feed axis of the machine. For example, there are ones due to the motor, such as a togging torque generated with respect to the rotor and a difference of a rotor rotation direction of the motor and ones due to a motor control apparatus, such as an operation program used to control the motor and a magnitude of a torque command. In addition, a workpiece machining condition in the machine tool having the motor as a drive source, a magnitude of a cutting load during machining by the machine tool, a temperature of the machine tool, vibrations generated when each of drive axes operates in the machine tool having the plurality of drive axes, and the like, also constitute to deterioration of the smoothness of feed of the feed axis of the machine.
For example, since a torque ripple occurs once relative to each single rotation of an electrical angle of the motor, deterioration of the smoothness of feed due to the torque ripple is periodic.
Hitherto, periodic deterioration of the smoothness of feed due to the torque ripple is reduced by compensating the torque ripple in advance with the number of corrections in a reverse phase. FIG. 11 is a diagram illustrating compensation of the torque ripple with the number of corrections in the reverse phase. In FIG. 11, the torque ripple generated with respect to the rotor is indicated by a solid line, the number of torque ripple corrections is indicated by a dotted line, and a waveform after compensation of the torque ripple with the number of torque ripple corrections is indicated by a single dot chain line. Relative to the torque ripple generated with respect to the rotor, superposing in advance the number of torque ripple corrections in the reverse phase on the torque command allows the torque ripple to be eliminated, whereby deterioration of the smoothness of feed can be reduced.
In addition, for example, there is a case where as disclosed in Japanese Unexamined Patent Publication (Kokai) No. H7-284286, to reduce deterioration of the smoothness of feed caused by the torque ripple, in a speed loop provided with a speed loop corrector, compensation is made with an equivalent of a torque variation, thereby correcting the torque command.
Since as described above, deterioration of the smoothness of feed due to the torque ripple is periodic, generating such number of corrections as to be in the reverse phase relative to the torque ripple to reduce deterioration of the smoothness of feed is easy. However, deterioration of the smoothness of feed due to the workpiece machining condition in the machine tool having the motor as a drive source, the magnitude of a cutting load during machining by the machine tool, the temperature of the machine tool, the vibrations generated when each of the drive axes operates in the machine tool having the plurality of drive axes, and the like is reproducible to some extent but not periodic. FIG. 12 is a diagram illustrating the number of errors that is not periodic. The number of errors that is not periodic cannot be corrected using the number of corrections in the reverse phase, such as the number of errors due to the torque ripple.